Paper Authors

Gül E. Okudan-Kremer
Pennsylvania State University, University Park

Gul Kremer is an Associate Professor of Engineering Design and Industrial Engineering at the Pennsylvania State University. She received her Ph.D. from University of Missouri, Rolla in Engineering Management and Systems Engineering. Her research interests include multi-criteria decision analysis methods applied to improvement of products and systems and enhancing creativity in engineering design settings. Her published work appears in journals such as Journal of Mechanical Design, Journal of Engineering Design, Journal of Intelligent Manufacturing, Journal of Engineering Education, European Journal of Engineering Education and Technovation. She is a member of IIE, ASME, and ASEE. She is also a National Research Council-US AFRL Summer Faculty Fellow for the Human Effectiveness Directorate (2002 - 2004), an invited participant of the National Academy of Engineering (NAE) Frontiers in Engineering Education Symposium (2009), and a Fulbright Scholar to Ireland (2010).

Linda C. Schmidt
University of Maryland, College Park

Linda C. Schmidt is an Associate Professor in the Department of Mechanical Engineering at the University of Maryland. Dr. Schmidt's research interests are in understanding the process by which early stage, engineering design tasks are successfully completed so that we can devise effective methods for learning design and preserving knowledge that arises in the process. She has been actively teaching and reflecting upon engineering design issues for over 15 years. Dr. Schmidt was the 2008 recipient of the American Society of Engineering Education's prestigious Fred Merryfield Design Award and is the co-author with George Dieter of the text "Engineering Design, 4th edition", published by McGraw Hill in 2008. Linda Schmidt has published over sixty refereed publications in the areas of mechanical design theory and methodology, mechanism design generation, graph isomorphism issues in generative design and effective student learning on engineering project design teams.

Abstract

An Investigation on the Impact of the Design Problem in Ideation Effectiveness ResearchWhile the quality of the undergraduate engineering programs has been monitored throughseveral means (e.g., ABET) in the US, in the face of grand challenges for engineering1, manyefforts have been undertaken to create the vision for what we should expect from ourundergraduate engineering students, and how we should help them live up to theseexpectations. For example, one of the significant reports2 discussing these issues indicatesthat our graduates should aspire “to have the ingenuity of Lillian Gilbreth, the problem-solving capabilities of Gordon Moore, the scientific insight of Albert Einstein, the creativityof Pablo Picasso, the determination of the Wright brothers, the leadership abilities of BillGates, the conscience of Eleanor Roosevelt, the vision of Martin Luther King, and thecuriosity and wonder of our grandchildren.” This statement implies that not only should ourgraduates be very well equipped with analytical skills but also master creative problemsolving. Indeed, given the projections3 that (1) the pace of technological innovation willcontinue to be rapid, (2) the world in which technology will be deployed will be intenselyglobally interconnected, and (3) designers, manufacturers, distributors, users will beincreasingly diverse and multidisciplinary; our graduates will need to develop these skills to ahigher degree.Despite this need, however, the engineering education has been observed to do the opposite,at least on enhancing creative problem solving skills. For example, students who need morecreative outlets have been observed to leave engineering programs 4, or engineering studentcreativity has been traced to decline5. Given this situation, several efforts have beenundertaken to stop and reverse this trend. One of these efforts is infusing engineeringcurriculum with systematic problem solving methods (TRIZ) and aiding design cognition andcreativity through design capture using manual or digital means (sketching), andsubsequently analyzing the effectiveness of introduced methods/tools in comparison tocontrol groups. In general, such experimentations are done in engineering design settings.In ideation effectiveness research settings, the design problem engineering students are facingcould present a bias factor. For example, the theme and or the context of the design problemmay or may not be familiar to the student (student’s ability to use relevant terminology andtap into existing knowledge), or the goal of the design problem may or may not be acceptableto the student which in turn might influence the motivation (humanitarian design projectsversus others). In this paper, we explore the potential dimensions of the design problems thatcould constitute biasing factors in design settings.We use data from our ongoing research (NSF CCLI 0920446) to compare the ideationeffectiveness results measured using quantitative means (quantity, novelty, and variety)1 Grand Challenges for Engineering, National Academy of Engineering, http://www.engineeringchallenges.org/,2008.2 The Engineer of 2020: Visions of Engineering in the New Century, National Academy of Engineering, ISBN-13:978-0-309-09162-6, 2004.3 The Engineer of 2020: Visions of Engineering in the New Century, National Academy of Engineering, ISBN-13:978-0-309-09162-6, 2004.4 Seymour, E. and Hewitt, N. (1996). Talking About Leaving: Why Undergraduates Leave the Sciences,Westview Press, ISBN-10: 0813389267.5 Masters, C., Hunter, S. and Okudan, G. (2009). Design Process Learning and Creative Processing: Is There aSynergy?, ASEE Conference Proceedings.across three different projects: (1) air-velocity controller design, (2) bio-mass cooker designfor rural communities, (2) traffic light redesign for snowy conditions. Based on our dataanalyses, we propose a familiarity and complexity matrix with which we represent theengineering design problems relative to the students who are tasked to solve them. Ourresults indicate that the familiarity and complexity dimensions as proposed help explain thedifferences across ideation effectiveness differences.

EndNote - RIS

TY - CPAPER
AB - An Investigation on the Impact of the Design Problem in Ideation Effectiveness ResearchWhile the quality of the undergraduate engineering programs has been monitored throughseveral means (e.g., ABET) in the US, in the face of grand challenges for engineering1, manyefforts have been undertaken to create the vision for what we should expect from ourundergraduate engineering students, and how we should help them live up to theseexpectations. For example, one of the significant reports2 discussing these issues indicatesthat our graduates should aspire “to have the ingenuity of Lillian Gilbreth, the problem-solving capabilities of Gordon Moore, the scientific insight of Albert Einstein, the creativityof Pablo Picasso, the determination of the Wright brothers, the leadership abilities of BillGates, the conscience of Eleanor Roosevelt, the vision of Martin Luther King, and thecuriosity and wonder of our grandchildren.” This statement implies that not only should ourgraduates be very well equipped with analytical skills but also master creative problemsolving. Indeed, given the projections3 that (1) the pace of technological innovation willcontinue to be rapid, (2) the world in which technology will be deployed will be intenselyglobally interconnected, and (3) designers, manufacturers, distributors, users will beincreasingly diverse and multidisciplinary; our graduates will need to develop these skills to ahigher degree.Despite this need, however, the engineering education has been observed to do the opposite,at least on enhancing creative problem solving skills. For example, students who need morecreative outlets have been observed to leave engineering programs 4, or engineering studentcreativity has been traced to decline5. Given this situation, several efforts have beenundertaken to stop and reverse this trend. One of these efforts is infusing engineeringcurriculum with systematic problem solving methods (TRIZ) and aiding design cognition andcreativity through design capture using manual or digital means (sketching), andsubsequently analyzing the effectiveness of introduced methods/tools in comparison tocontrol groups. In general, such experimentations are done in engineering design settings.In ideation effectiveness research settings, the design problem engineering students are facingcould present a bias factor. For example, the theme and or the context of the design problemmay or may not be familiar to the student (student’s ability to use relevant terminology andtap into existing knowledge), or the goal of the design problem may or may not be acceptableto the student which in turn might influence the motivation (humanitarian design projectsversus others). In this paper, we explore the potential dimensions of the design problems thatcould constitute biasing factors in design settings.We use data from our ongoing research (NSF CCLI 0920446) to compare the ideationeffectiveness results measured using quantitative means (quantity, novelty, and variety)1 Grand Challenges for Engineering, National Academy of Engineering, http://www.engineeringchallenges.org/,2008.2 The Engineer of 2020: Visions of Engineering in the New Century, National Academy of Engineering, ISBN-13:978-0-309-09162-6, 2004.3 The Engineer of 2020: Visions of Engineering in the New Century, National Academy of Engineering, ISBN-13:978-0-309-09162-6, 2004.4 Seymour, E. and Hewitt, N. (1996). Talking About Leaving: Why Undergraduates Leave the Sciences,Westview Press, ISBN-10: 0813389267.5 Masters, C., Hunter, S. and Okudan, G. (2009). Design Process Learning and Creative Processing: Is There aSynergy?, ASEE Conference Proceedings.across three different projects: (1) air-velocity controller design, (2) bio-mass cooker designfor rural communities, (2) traffic light redesign for snowy conditions. Based on our dataanalyses, we propose a familiarity and complexity matrix with which we represent theengineering design problems relative to the students who are tasked to solve them. Ourresults indicate that the familiarity and complexity dimensions as proposed help explain thedifferences across ideation effectiveness differences.
AU - Gül E. Okudan-Kremer
AU - Linda C. Schmidt
AU - Noe Vargas Hernandez
CY - Vancouver, BC
DA - 2011/06/26
PB - ASEE Conferences
TI - An Investigation on the Impact of the Design Problem in Ideation Effectiveness Research
UR - https://peer.asee.org/17472
ER -